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Correspondence regarding Zhong et al., BMC Bioinformatics 2013 Mar 7;14:89.
MedLine Citation:
PMID:  25431099     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Computational expression deconvolution aims to estimate the contribution of individual cell populations to expression profiles measured in samples of heterogeneous composition. Zhong et al. recently proposed Digital Sorting Algorithm (BMC Bioinformatics 2013 Mar 7;14:89) and showed that they could accurately estimate population-specific expression levels and expression differences between two populations. They compared DSA with Population-Specific Expression Analysis (PSEA), a previous deconvolution method that we developed to detect expression changes occurring within the same population between two conditions (e.g. disease versus non-disease). However, Zhong et al. compared PSEA-derived specific expression levels across different cell populations. Specific expression levels obtained with PSEA cannot be directly compared across different populations as they are on a relative scale. They are accurate as we demonstrate by deconvolving the same dataset used by Zhong et al. and, importantly, allow for comparison of population-specific expression across conditions.
Authors:
Alexandre Kuhn
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Publication Detail:
Type:  Journal Article     Date:  2014-11-28
Journal Detail:
Title:  BMC bioinformatics     Volume:  15     ISSN:  1471-2105     ISO Abbreviation:  BMC Bioinformatics     Publication Date:  2014  
Date Detail:
Created Date:  2014-11-28     Completed Date:  -     Revised Date:  2014-12-01    
Medline Journal Info:
Nlm Unique ID:  100965194     Medline TA:  BMC Bioinformatics     Country:  England    
Other Details:
Languages:  eng     Pagination:  347     Citation Subset:  IM    
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